COFFEE - AN OBJECTIVE FUNCTION FOR MULTIPLE SEQUENCE ALIGNMENTS

Motivation: In order to inn-ease rite accuracy of multiple sequence al ignments, we designed a new strategy for optimizing multiple sequence alignments by genetic algorithm. We named it COFFEE (Consistency based Objective Function For alignmEnt Evaluation). The COFFEE score reflec ts the level of consistency between a multiple sequence alignment and a library containing pairwise alignments of the same sequences. Result s: We show that multiple sequence alignments can be optimized for thei r COFFEE score with the genetic algorithm package SAGA. The COFFEE fun ction is tested on 11 test cases made of structural alignments extract ed from 3D ali. These alignments are compared to those produced using five alternative methods. Results indicate that COFFEE outperforms the other methods when the level of identity between the sequences is low . Accuracy is evaluated by comparison with the structural alignments u sed as references. We also show that the COFFEE score can be used as a reliability index on multiple sequence alignments. Finally, we show t hat given a library of structure-based painwise sequence alignments ex tracted fi-om FSSP, SAGA cart produce high-quality multiple sequence a lignments. The main advantage of COFFEE is irs flexibility. With COFFE E, any method suitable for making pairwise alignments can be extended to making multiple alignments.